{"id":"W4372203220","doi":"10.1155/2023/3633058","title":"Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hebei Provincial Department of Transportation; Hebei Provincial Department of Bureau of Science and Technology; U.S. Department of Transportation","keywords":"CarSim; Artificial neural network; Computer science; Estimation; Convolutional neural network; Simulation; Artificial intelligence; Engineering; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002964946,0.000758218,0.0005493621,0.0008022999,0.0003123775,0.0005280385,0.001008983,0.000452152,0.0009474561],"category_scores_gemma":[0.0008980125,0.000407449,0.0004982057,0.0006329435,0.0002918777,0.00147404,0.0008792739,0.0007332464,0.0002308012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006217957,"about_ca_system_score_gemma":0.0006864596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01611124,"about_ca_topic_score_gemma":0.01332932,"domain_scores_codex":[0.9997538,0.00002348615,0.00001341671,0.00009659044,0.00007049456,0.00004222885],"domain_scores_gemma":[0.9997368,0.00005260611,0.00005056239,0.00003293444,0.0001055419,0.0000214696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107095,0.0001815427,0.01706323,0.0001017843,0.00011658,0.0001654045,0.0001332423,0.7474098,0.01755398,0.002769356,0.001458769,0.2129356],"study_design_scores_gemma":[0.00000178983,0.00001565386,0.001449489,0.000002144357,0.000008641267,0.00001393046,0.000009990832,0.9963664,0.001584944,0.0003668981,0.0001750209,0.00000508689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.136588,0.0002707588,0.8592328,0.0001094406,0.00005438153,0.00004266738,0.0001139717,0.0008246104,0.002763386],"genre_scores_gemma":[0.9750425,0.0001276599,0.02302073,0.00002782856,0.00001768061,0.00003483855,0.0001463938,0.00002510562,0.001557451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01611124,"threshold_uncertainty_score":0.03203493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006110203113580785,"score_gpt":0.2310807484789076,"score_spread":0.2249705453653268,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}